Method and system for classifying objects around vehicle

1. A method for classifying objects around a vehicle, the method comprising: generating a dynamic occupancy grid map comprising a plurality of cells comprising point data corresponding to each of a plurality of objects located around the vehicle and velocity vector information of the point data, based on LiDAR data received from a LiDAR sensor of the vehicle and information related to movement of the vehicle; determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique; and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster and velocity vector information included in cells belonging to the cluster.

2. The method according to claim 1, wherein generating the dynamic occupancy grid map comprises: generating an occupancy grid map comprising a plurality of grid cells comprising the point data corresponding to each of the plurality of objects, based on the LiDAR data; and generating the dynamic occupancy grid map by mapping the velocity vector information of the point data calculated based on the information related to the movement of the vehicle to the occupancy grid map.

3. The method according to claim 2, wherein the velocity vector information is calculated based on velocity information acquired based on past and present position information of the point data and elapsed time and velocity information of the vehicle included in the information related to the movement of the vehicle.

4. The method according to claim 1, further comprising assigning object identification information to the cluster corresponding to each of the plurality of objects.

5. The method according to claim 1, wherein classifying the object corresponding to the cluster into the static object or the dynamic object comprises: determining, among the objects, a first object as the static object based on a region size of the cluster corresponding to the first object being greater than a first predetermined threshold; and determining, among the objects, a second object as the static object or the dynamic object based on a region size of the cluster corresponding to the second object being equal to or less than the first predetermined threshold and based on velocity vector information included in cells corresponding to the second object.

6. The method according to claim 5, wherein determining the second object as the static object or the dynamic object comprises: identifying whether or not a variance value of directions of velocity vectors is greater than a second predetermined threshold based on the velocity vector information included in the cells corresponding to the second object; determining the second object as the static object when the variance value is greater than the second predetermined threshold; and determining the second object as the dynamic object when the variance value is equal to or less than the second predetermined threshold.

7. The method according to claim 1, wherein determining the cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map comprises determining, among respective point data within a predetermined range from arbitrary point data in the plurality of cells, second point data having a speed difference with arbitrary first point data that is equal to or less than a predetermined threshold, as one cluster based on the velocity vector information.

8. A system for classifying objects around a vehicle, the system comprising: an interface configured to receive LiDAR data from a LiDAR sensor of the vehicle; and a controller electrically connected to the interface, wherein the controller is configured to: generate a dynamic occupancy grid map comprising a plurality of cells comprising point data corresponding to each of a plurality of objects located around the vehicle and velocity vector information of the point data, based on the LiDAR data and information related to movement of the vehicle; determine a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique; and classify an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster and velocity vector information included in cells belonging to the cluster.

9. The system according to claim 8, wherein the controller is configured to: generate an occupancy grid map comprising a plurality of grid cells comprising the point data corresponding to each of the plurality of objects, based on the LiDAR data; and generate the dynamic occupancy grid map by mapping the velocity vector information of the point data calculated based on the information related to the movement of the vehicle to the occupancy grid map.

10. The system according to claim 9, wherein the velocity vector information is calculated based on velocity information acquired based on past and present position information of the point data and elapsed time and velocity information of the vehicle included in the information related to the movement of the vehicle.

11. The system according to claim 8, wherein the controller is configured to assign object identification information to the cluster corresponding to each of the plurality of objects.

12. The system according to claim 8, wherein the controller is configured to: determine, among the objects, a first object as the static object based on a region size of the cluster corresponding to the first object being greater than a first predetermined threshold; and determine, among the objects, a second object as the static object or the dynamic object based on a region size of the cluster corresponding to the second object being equal to or less than the first predetermined threshold and based on velocity vector information included in cells corresponding to the second object.

13. The system according to claim 12, wherein the controller is configured to: identify whether or not a variance value of directions of velocity vectors is greater than a second predetermined threshold based on the velocity vector information included in the cells corresponding to the second object; determine the second object as the static object when the variance value is greater than the second predetermined threshold; and determine the second object as the dynamic object when the variance value is equal to or less than the second predetermined threshold.

14. The system according to claim 8, wherein the controller is configured to determine the cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map by determining, among respective point data within a predetermined range from arbitrary point data in the plurality of cells, second point data having a speed difference with arbitrary first point data that is equal to or less than a predetermined threshold, as one cluster based on the velocity vector information.

15. A vehicle comprising: a vehicle body; a sensor unit connected to the vehicle body, the sensor unit comprising a camera and a LiDAR sensor; an interface configured to receive data from the sensor unit; a memory storing data; and a controller electrically connected to the interface and the memory, wherein the controller is configured to: generate a dynamic occupancy grid map comprising a plurality of cells comprising point data corresponding to each of a plurality of objects located around the vehicle and velocity vector information of the point data, based on the data and information related to movement of the vehicle; determine a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique; and classify an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster and velocity vector information included in cells belonging to the cluster.

16. The vehicle according to claim 15, wherein the controller is configured to: generate an occupancy grid map comprising a plurality of grid cells comprising the point data corresponding to each of the plurality of objects, based on the data; and generate the dynamic occupancy grid map by mapping the velocity vector information of the point data calculated based on the information related to the movement of the vehicle to the occupancy grid map.

17. The vehicle according to claim 16, wherein the velocity vector information is calculated based on velocity information acquired based on past and present position information of the point data and elapsed time and velocity information of the vehicle included in the information related to the movement of the vehicle.

18. The vehicle according to claim 15, wherein the controller is configured to assign object identification information to the cluster corresponding to each of the plurality of objects.

19. The vehicle according to claim 15, wherein the controller is configured to: determine, among the objects, a first object as the static object based on a region size of the cluster corresponding to the first object being greater than a first predetermined threshold; determine, among the objects, a second object as the static object or the dynamic object based on a region size of the cluster corresponding to the second object being equal to or less than the first predetermined threshold and based on velocity vector information included in cells corresponding to the second object; identify whether or not a variance value of directions of velocity vectors is greater than a second predetermined threshold based on the velocity vector information included in the cells corresponding to the second object; determine the second object as the static object when the variance value is greater than the second predetermined threshold; and determine the second object as the dynamic object when the variance value is equal to or less than the second predetermined threshold.

20. The vehicle according to claim 15, wherein the controller is configured to determine the cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map by determining, among respective point data within a predetermined range from arbitrary point data in the plurality of cells, second point data having a speed difference with arbitrary first point data that is equal to or less than a predetermined threshold, as one cluster based on the velocity vector information.